A Review on Various Energy Efficient Techniques in Cloud Environment

Cloud computing is web based mostly development and use of engineering. it is a mode of computing within which dynamically scalable and sometimes virtualized resources are provided as a service over the web. Users needn't have data of, experience in, or management over the technology infrastructure "in the cloud" that supports them. programming is one of the core steps to with efficiency exploit the capabilities of heterogeneous computing systems. On cloud computing platform, load equalisation of the whole system will be dynamically handled by using virtualization technology through that it becomes potential to remap virtual machine and physical resources in step with the modification in load. However, so as to boost performance, the virtual machines ought to totally utilize its resources and services by adapting to computing setting dynamically. The load balancing with correct allocation of resources should be bonded so as to boost resource utility and energy efficiency

Leveraging heterogeneity for energy minimization in data centers

Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research

A hybrid genetic algorithm with mapreduce technique for cloud computing energy efficiency

Computer clouds generally comprise large power-consuming data centers as they are designed to support the elasticity and scalability required by customers. However, while cloud computing reduces energy consumption for customers, it is an issue for providers who have to deal with increasing demand and performance expectations. This creates the need for mechanisms to improve the energy-efficiency of cloud computing data centers while maintaining desired levels of performance. This research seeks to formulate a hybrid algorithm based on Genetic algorithm and MapReduce algorithm techniques to further promote energy efficiency in the cloud computing platform. The function of the MapReduce algorithm is to optimize scheduling performance which is one of the more efficient techniques for handling large data in servers. Genetic algorithm is effective in optimally measuring the value of operations and allows for the minimization of energy efficiency where it includes the formula for single optimization energy efficiency. A series of simulations were developed to evaluate the effectiveness of the proposed algorithm. The evaluation results show the amount of Information Technology equipment power required for Power Usage Effectiveness values to optimize energy usage where the performance of the proposed algorithm is 6% better than the previous genetic algorithm and 5% better than Hadoop MapReduce scheduling on low load conditions. On the other hand, the proposed algorithm improved energy efficiency in comparison with the previous work

Implementation and Evaluation of a Thermal-Aware Campus Grid

Volunteer and grid computing framework systems have helped create several of today’s largest resource pools for scientific and engineering computing. At the same time, as computers evolve, there is a greater demand for more environmental-aware scheduling systems to be deployed with these frameworks in order to address existing concerns of the preservation of resources like energy, and computers themselves. In response to this concern, the High Performance Computing Services Center of Universiti Teknologi PETRONAS (HPC-UTP) sought to incorporate thermal-aware scheduling into its campus grid. To achieve this, the center decided to modify an existing Volunteer Computing (VC) framework to make use of different schedulers at run time, without the need to recompile the server. This thermal-aware, dynamic-scheduling capable framework will be deployed on a test environment, to assess its viability for the university’s campus grid

Implementation and Evaluation of a Thermal-Aware Campus Grid

Volunteer and grid computing framework systems have helped create several of today’s largest resource pools for scientific and engineering computing. At the same time, as computers evolve, there is a greater demand for more environmental-aware scheduling systems to be deployed with these frameworks in order to address existing concerns of the preservation of resources like energy, and computers themselves. In response to this concern, the High Performance Computing Services Center of Universiti Teknologi PETRONAS (HPC-UTP) sought to incorporate thermal-aware scheduling into its campus grid. To achieve this, the center decided to modify an existing Volunteer Computing (VC) framework to make use of different schedulers at run time, without the need to recompile the server. This thermal-aware, dynamic-scheduling capable framework will be deployed on a test environment, to assess its viability for the university’s campus grid

Shadow Price Guided Genetic Algorithms

The Genetic Algorithm (GA) is a popular global search algorithm. Although it has been used successfully in many fields, there are still performance challenges that prevent GA’s further success. The performance challenges include: difficult to reach optimal solutions for complex problems and take a very long time to solve difficult problems. This dissertation is to research new ways to improve GA’s performance on solution quality and convergence speed. The main focus is to present the concept of shadow price and propose a two-measurement GA. The new algorithm uses the fitness value to measure solutions and shadow price to evaluate components. New shadow price Guided operators are used to achieve good measurable evolutions. Simulation results have shown that the new shadow price Guided genetic algorithm (SGA) is effective in terms of performance and efficient in terms of speed